AIStor eliminates software abstraction overhead by harnessing modern server capabilities across CPU, memory, disk, and network. Delivers bare-metal speeds while maintaining enterprise reliability.
Hardware acceleration spanning CPU compute, memory architecture, disk I/O, and intelligent caching systems.
SIMD Acceleration
CPU compute optimization through vectorized instructions that accelerate erasure coding and data processing operations.
Zero-Copy Architecture
Memory-aligned buffer pools eliminate redundant data copies, maximizing throughput per server and reducing infrastructure costs
Direct Memory Access
O_DIRECT disk I/O bypasses kernel overhead, eliminating unpredictable latency spikes from page cache eviction.
Low-Latency Metadata Cache
Ring buffer architecture with 4,096 shards delivers 1.5ms GC pauses at 20 million entries—6.2x faster than traditional implementations.
Metadata Cache Performance
AIStor's specialized cache architecture uses byte array operations instead of standard Go maps, allowing the garbage collector to treat large cache structures as single pointers rather than scanning millions of individual entries. This design maintains consistent performance under massive concurrent workloads.
GC Pause Times
1.5ms GC pause times with 20M entries
Parallel Shards
4,096 shards for parallel operations
Cache Sizing
Configurable cache size via MINIO_DRIVE_CACHE_SIZE
Analytics Support
Optimized for Trino and Spark workloads
Get the Complete Technical Details
Deep dive into AIStor's hardware optimization architecture, including SIMD integration, buffer management subsystems, and cache implementation specifics
Traditional object stores pursue lowest-common-denominator portability, avoiding platform-specific optimizations that complicate cross-platform support. This conservative approach forces software abstraction layers that impose significant performance penalties. AIStor embraces hardware-first design, making platform-specific acceleration the default rather than optional.
6.2x
Faster GC Pauses
Byte array cache vs. traditional map-based implementations
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